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- """The two-step game from QMIX: https://arxiv.org/pdf/1803.11485.pdf
- Configurations you can try:
- - normal policy gradients (PG)
- - contrib/MADDPG
- - QMIX
- See also: centralized_critic.py for centralized critic PPO on this game.
- """
- import argparse
- from gym.spaces import Dict, Discrete, Tuple, MultiDiscrete
- import os
- import ray
- from ray import tune
- from ray.tune import register_env, grid_search
- from ray.rllib.env.multi_agent_env import ENV_STATE
- from ray.rllib.examples.env.two_step_game import TwoStepGame
- from ray.rllib.policy.policy import PolicySpec
- from ray.rllib.utils.test_utils import check_learning_achieved
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--run",
- type=str,
- default="PG",
- help="The RLlib-registered algorithm to use.")
- parser.add_argument(
- "--framework",
- choices=["tf", "tf2", "tfe", "torch"],
- default="tf",
- help="The DL framework specifier.")
- parser.add_argument("--num-cpus", type=int, default=0)
- parser.add_argument(
- "--as-test",
- action="store_true",
- help="Whether this script should be run as a test: --stop-reward must "
- "be achieved within --stop-timesteps AND --stop-iters.")
- parser.add_argument(
- "--stop-iters",
- type=int,
- default=200,
- help="Number of iterations to train.")
- parser.add_argument(
- "--stop-timesteps",
- type=int,
- default=50000,
- help="Number of timesteps to train.")
- parser.add_argument(
- "--stop-reward",
- type=float,
- default=7.0,
- help="Reward at which we stop training.")
- parser.add_argument(
- "--local-mode",
- action="store_true",
- help="Init Ray in local mode for easier debugging.")
- if __name__ == "__main__":
- args = parser.parse_args()
- ray.init(num_cpus=args.num_cpus or None, local_mode=args.local_mode)
- grouping = {
- "group_1": [0, 1],
- }
- obs_space = Tuple([
- Dict({
- "obs": MultiDiscrete([2, 2, 2, 3]),
- ENV_STATE: MultiDiscrete([2, 2, 2])
- }),
- Dict({
- "obs": MultiDiscrete([2, 2, 2, 3]),
- ENV_STATE: MultiDiscrete([2, 2, 2])
- }),
- ])
- act_space = Tuple([
- TwoStepGame.action_space,
- TwoStepGame.action_space,
- ])
- register_env(
- "grouped_twostep",
- lambda config: TwoStepGame(config).with_agent_groups(
- grouping, obs_space=obs_space, act_space=act_space))
- if args.run == "contrib/MADDPG":
- obs_space = Discrete(6)
- act_space = TwoStepGame.action_space
- config = {
- "learning_starts": 100,
- "env_config": {
- "actions_are_logits": True,
- },
- "multiagent": {
- "policies": {
- "pol1": PolicySpec(
- observation_space=obs_space,
- action_space=act_space,
- config={"agent_id": 0}),
- "pol2": PolicySpec(
- observation_space=obs_space,
- action_space=act_space,
- config={"agent_id": 1}),
- },
- "policy_mapping_fn": (
- lambda aid, **kwargs: "pol2" if aid else "pol1"),
- },
- "framework": args.framework,
- # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
- "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
- }
- group = False
- elif args.run == "QMIX":
- config = {
- "rollout_fragment_length": 4,
- "train_batch_size": 32,
- "exploration_config": {
- "epsilon_timesteps": 5000,
- "final_epsilon": 0.05,
- },
- "num_workers": 0,
- "mixer": grid_search([None, "qmix"]),
- "env_config": {
- "separate_state_space": True,
- "one_hot_state_encoding": True
- },
- # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
- "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
- }
- group = True
- else:
- config = {
- # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
- "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
- "framework": args.framework,
- }
- group = False
- stop = {
- "episode_reward_mean": args.stop_reward,
- "timesteps_total": args.stop_timesteps,
- "training_iteration": args.stop_iters,
- }
- config = dict(config, **{
- "env": "grouped_twostep" if group else TwoStepGame,
- })
- if args.as_test:
- config["seed"] = 1234
- results = tune.run(args.run, stop=stop, config=config, verbose=2)
- if args.as_test:
- check_learning_achieved(results, args.stop_reward)
- ray.shutdown()
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